Enter the bit wars: A study of video game marketing and platform crafting in the wake of the TurboGrafx-16 launch
Bibliographic record
Abstract
NEC’s TurboGrafx-16 console, as its very name suggests, was caught in the middle of loaded discourses about technology. In this article, we seek to reflect on the initial encounter between the player and any given platform, the one that occurs through a marketed image. The authors of Digital Play insisted already in 2003 that it is essential to study the interactions between technology and marketing practices to better understand the video game experience. More recently, James Newman has demonstrated the significant role of the discourse produced by the industry and the dedicated video game press in shaping the contemporary culture of material obsolescence. The introduction of the TurboGrafx-16 at the end of the 1980s partakes in a discursive framing of technology whose impact can still be felt in the way we construct history today: It helped solidify the widespread adoption of the biological metaphor (console “generations”), and of the technological warfare rhetoric.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".